TY - JOUR
T1 - Response surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design
AU - Canessa, E.
AU - Chaigneau, S.
N1 - Publisher Copyright:
© 2017, Revista Ingenieria e Investigacion - Editorial Board. All rights reserved.
PY - 2017
Y1 - 2017
N2 - We present an improved Pareto Genetic Algorithm (PGA), which finds solutions to problems of robust design in multi-response systems with 4 responses and as many as 10 control and 5 noise factors. Because some response values might not have been obtained in the robust design experiment and are needed in the search process, the PGA uses Response Surface Methodology (RSM) to estimate them. Not only the PGA delivered solutions that adequately adjusted the response means to their target values, and with low variability, but also found more Pareto efficient solutions than a previous version of the PGA. This improvement makes it easier to find solutions that meet the trade-off among variance reduction, mean adjustment and economic considerations. Furthermore, RSM allows estimating outputs’ means and variances in highly non-linear systems, making the new PGA appropriate for such systems.
AB - We present an improved Pareto Genetic Algorithm (PGA), which finds solutions to problems of robust design in multi-response systems with 4 responses and as many as 10 control and 5 noise factors. Because some response values might not have been obtained in the robust design experiment and are needed in the search process, the PGA uses Response Surface Methodology (RSM) to estimate them. Not only the PGA delivered solutions that adequately adjusted the response means to their target values, and with low variability, but also found more Pareto efficient solutions than a previous version of the PGA. This improvement makes it easier to find solutions that meet the trade-off among variance reduction, mean adjustment and economic considerations. Furthermore, RSM allows estimating outputs’ means and variances in highly non-linear systems, making the new PGA appropriate for such systems.
KW - Parameter design
KW - Pareto genetic algorithm
KW - Response surface methodology
KW - Robust design
UR - http://www.scopus.com/inward/record.url?scp=85027275585&partnerID=8YFLogxK
U2 - 10.15446/ing.investig.v37n2.57152
DO - 10.15446/ing.investig.v37n2.57152
M3 - Article
AN - SCOPUS:85027275585
SN - 0120-5609
VL - 37
SP - 89
EP - 98
JO - Ingenieria e Investigacion
JF - Ingenieria e Investigacion
IS - 2
ER -